Update roadmap for v2.6.0

ROADMAP.md

@@ -8,9 +8,9 @@ For past changes and release history, see [CHANGELOG.md](CHANGELOG.md).
 
 ## Current Status
 
-diff-diff v2.4.1 is a **production-ready** DiD library with feature parity with R's `did` + `HonestDiD` + `synthdid` ecosystem for core DiD analysis:
+diff-diff v2.6.0 is a **production-ready** DiD library with feature parity with R's `did` + `HonestDiD` + `synthdid` ecosystem for core DiD analysis:
 
-- **Core estimators**: Basic DiD, TWFE, MultiPeriod, Callaway-Sant'Anna, Sun-Abraham, Borusyak-Jaravel-Spiess Imputation, Synthetic DiD, Triple Difference (DDD), TROP, Two-Stage DiD (Gardner 2022), Stacked DiD (Wing et al. 2024)
+- **Core estimators**: Basic DiD, TWFE, MultiPeriod, Callaway-Sant'Anna, Sun-Abraham, Borusyak-Jaravel-Spiess Imputation, Synthetic DiD, Triple Difference (DDD), TROP, Two-Stage DiD (Gardner 2022), Stacked DiD (Wing et al. 2024), Continuous DiD (Callaway, Goodman-Bacon & Sant'Anna 2024)
 - **Valid inference**: Robust SEs, cluster SEs, wild bootstrap, multiplier bootstrap, placebo-based variance
 - **Assumption diagnostics**: Parallel trends tests, placebo tests, Goodman-Bacon decomposition
 - **Sensitivity analysis**: Honest DiD (Rambachan-Roth), Pre-trends power analysis (Roth 2022)
@@ -20,35 +20,15 @@ diff-diff v2.4.1 is a **production-ready** DiD library with feature parity with
 
 ---
 
-## Near-Term Enhancements (v2.5)
-
-High-value additions building on our existing foundation.
-
-### ~~Stacked Difference-in-Differences~~ (Implemented in v2.5)
-
-Implemented as `StackedDiD`. See `diff_diff/stacked_did.py`.
+## Near-Term Enhancements (v2.7)
 
 ### Staggered Triple Difference (DDD)
 
-Extend the existing `TripleDifference` estimator to handle staggered adoption settings. The current implementation handles 2-period DDD; this extends to multi-period designs.
+Extend the existing `TripleDifference` estimator to handle staggered adoption settings.
 
-**Multi-period/Staggered Support:**
 - Group-time ATT(g,t) for DDD designs with variation in treatment timing
-- Handle settings where groups adopt at different times
-- Multiple comparison groups (never-treated, not-yet-treated in either dimension)
-- `StaggeredTripleDifference` class or extended `TripleDifference` with `first_treat` parameter
-
-**Event Study Aggregation:**
-- Dynamic treatment effects over time (event study coefficients)
-- Pre-treatment placebo effects for parallel trends assessment
-- `aggregate='event_study'` parameter like `CallawaySantAnna`
-- Integration with `plot_event_study()` visualization
-
-**Multiplier Bootstrap Inference:**
+- Event study aggregation and pre-treatment placebo effects
 - Multiplier bootstrap for valid inference in staggered settings
-- Rademacher, Mammen, and Webb weight options (matching existing estimators)
-- `n_bootstrap` parameter and `DDDBootstrapResults` class
-- Clustered bootstrap for panel data
 
 **Reference**: [Ortiz-Villavicencio & Sant'Anna (2025)](https://arxiv.org/abs/2505.09942). *Working Paper*. R package: `triplediff`.
 
@@ -60,22 +40,13 @@ Extend the existing `TripleDifference` estimator to handle staggered adoption se
 
 ---
 
-## Medium-Term Enhancements (v2.5+)
-
-Extending diff-diff to handle more complex settings.
+## Medium-Term Enhancements
 
-### Continuous Treatment DiD
+### Efficient DiD Estimators
 
-Many treatments have dose/intensity rather than binary on/off. Active research area with recent breakthroughs.
+Semiparametrically efficient versions of existing DiD/event-study estimators with 40%+ precision gains over current methods.
 
-- Treatment effect on treated (ATT) parameters under generalized parallel trends
-- Dose-response curves and marginal effects
-- Handle settings where "dose" varies across units and time
-- Event studies with continuous treatments
-
-**References**:
-- [Callaway, Goodman-Bacon & Sant'Anna (2024)](https://arxiv.org/abs/2107.02637). *NBER Working Paper*.
-- [de Chaisemartin, D'Haultfœuille & Vazquez-Bare (2024)](https://arxiv.org/abs/2402.05432). *AEA Papers and Proceedings*.
+**Reference**: [Chen, Sant'Anna & Xie (2025)](https://arxiv.org/abs/2506.17729). *Working Paper*.
 
 ### de Chaisemartin-D'Haultfœuille Estimator
 
@@ -84,7 +55,6 @@ Handles treatment that switches on and off (reversible treatments), unlike most
 - Allows units to move into and out of treatment
 - Time-varying, heterogeneous treatment effects
 - Comparison with never-switchers or flexible control groups
-- Different assumptions than CS/SA—useful for different settings
 
 **Reference**: [de Chaisemartin & D'Haultfœuille (2020, 2024)](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3980758). *American Economic Review*.
 
@@ -95,7 +65,6 @@ Implements local projections for dynamic treatment effects. Doesn't require spec
 - Flexible impulse response estimation
 - Robust to misspecification of dynamics
 - Natural handling of anticipation effects
-- Growing use in macroeconomics and policy evaluation
 
 **Reference**: Dube, Girardi, Jordà, and Taylor (2023).
 
@@ -105,30 +74,16 @@ For outcomes where linear models are inappropriate (binary, count, bounded).
 
 - Logit/probit DiD for binary outcomes
 - Poisson DiD for count outcomes
-- Flexible strategies for staggered designs with nonlinear models
 - Proper handling of incidence rate ratios and odds ratios
 
 **Reference**: [Wooldridge (2023)](https://academic.oup.com/ectj/article/26/3/C31/7250479). *The Econometrics Journal*.
 
-### Doubly Robust DiD + Synthetic Control
-
-Unified framework combining DiD and synthetic control with doubly robust identification—valid under *either* parallel trends or synthetic control assumptions.
-
-- ATT identified under parallel trends OR group-level SC condition
-- Semiparametric estimation framework
-- Multiplier bootstrap for valid inference under either assumption
-- Strengthens credibility by avoiding the DiD vs. SC trade-off
-
-**Reference**: [Sun, Xie & Zhang (2025)](https://arxiv.org/abs/2503.11375). *Working Paper*.
-
 ### Causal Duration Analysis with DiD
 
 Extends DiD to duration/survival outcomes where standard methods fail (hazard rates, time-to-event).
 
 - Duration analogue of parallel trends on hazard rates
 - Avoids distributional assumptions and hazard function specification
-- Visual and formal pre-trends assessment for duration data
-- Handles absorbing states approaching probability bounds
 
 **Reference**: [Deaner & Ku (2025)](https://www.aeaweb.org/conference/2025/program/paper/k77Kh8iS). *AEA Conference Paper*.
 
@@ -138,38 +93,56 @@ Extends DiD to duration/survival outcomes where standard methods fail (hazard ra
 
 Frontier methods requiring more research investment.
 
-### Matrix Completion Methods
+### DiD with Interference / Spillovers
 
-Unified framework encompassing synthetic control and regression approaches. Moves seamlessly between cross-sectional and time-series patterns.
+Standard DiD assumes SUTVA; spatial/network spillovers violate this. Two-stage imputation approach estimates treatment AND spillover effects under staggered timing.
 
-- Nuclear norm regularization for low-rank structure
-- Handles missing data patterns common in panel settings
-- Bridges synthetic control (few units, many periods) and regression (many units, few periods)
-- Confidence intervals via debiasing
+**Reference**: [Butts (2024)](https://arxiv.org/abs/2105.03737). *Working Paper*.
 
-**Reference**: [Athey et al. (2021)](https://arxiv.org/abs/1710.10251). *Journal of the American Statistical Association*.
+### Quantile/Distributional DiD
+
+Recover the full counterfactual distribution and quantile treatment effects (QTT), not just mean ATT. Goes beyond "what's the average effect" to "who gains, who loses."
+
+- Changes-in-Changes (CiC) identification strategy
+- QTT(τ) at user-specified quantiles
+- Full counterfactual distribution function
+- Two-period foundation, then staggered extension
+
+**Reference**: [Athey & Imbens (2006)](https://onlinelibrary.wiley.com/doi/10.1111/j.1468-0262.2006.00668.x). *Econometrica*.
+
+### CATT Meta-Learner for Heterogeneous Effects
+
+ML-powered conditional ATT — discover who benefits most from treatment using doubly robust meta-learner.
+
+**Reference**: [Lan, Chang, Dillon & Syrgkanis (2025)](https://arxiv.org/abs/2502.04699). *Working Paper*.
 
 ### Causal Forests for DiD
 
 Machine learning methods for discovering heterogeneous treatment effects in DiD settings.
 
 - Estimate treatment effect heterogeneity across covariates
 - Data-driven subgroup discovery
-- Combine with DiD identification for observational data
 - Honest confidence intervals for discovered heterogeneity
 
 **References**:
 - [Kattenberg, Scheer & Thiel (2023)](https://ideas.repec.org/p/cpb/discus/452.html). *CPB Discussion Paper*.
 - Athey & Wager (2019). *Annals of Statistics*.
 
+### Matrix Completion Methods
+
+Unified framework encompassing synthetic control and regression approaches.
+
+- Nuclear norm regularization for low-rank structure
+- Bridges synthetic control (few units, many periods) and regression (many units, few periods)
+
+**Reference**: [Athey et al. (2021)](https://arxiv.org/abs/1710.10251). *Journal of the American Statistical Association*.
+
 ### Double/Debiased ML for DiD
 
 For high-dimensional settings with many potential confounders.
 
 - ML for nuisance parameter estimation (propensity, outcome models)
 - Cross-fitting for valid inference
-- Handles many covariates without overfitting concerns
-- Doubly-robust estimation with ML flexibility
 
 **Reference**: Chernozhukov et al. (2018). *The Econometrics Journal*.
 
@@ -183,10 +156,6 @@ For high-dimensional settings with many potential confounders.
 
 ## Infrastructure Improvements
 
-Ongoing maintenance and developer experience.
-
-### Documentation
-
 - Video tutorials and worked examples
 
 ---